18 citations · 23 across the 3 of their papers we have counts for
5 papers
Addressing Class Imbalance in Scene Graph Parsing by Learning to Contrast and Score
He Huang, Shunta Saito, Yuta Kikuchi +3
Scene graph parsing aims to detect objects in an image scene and recognize their relations. Recent approaches have achieved high average scores on some popular benchmarks, but fail…
Chainer: A Deep Learning Framework for Accelerating the Research Cycle
Seiya Tokui, Ryosuke Okuta, Takuya Akiba +7
Software frameworks for neural networks play a key role in the development and application of deep learning methods. In this paper, we introduce the Chainer framework, which intend…
Train Sparsely, Generate Densely: Memory-efficient Unsupervised Training of High-resolution Temporal GAN
Masaki Saito, Shunta Saito, Masanori Koyama +1
Training of Generative Adversarial Network (GAN) on a video dataset is a challenge because of the sheer size of the dataset and the complexity of each observation. In general, the…
ChainerCV: a Library for Deep Learning in Computer Vision
Yusuke Niitani, Toru Ogawa, Shunta Saito +1
Despite significant progress of deep learning in the field of computer vision, there has not been a software library that covers these methods in a unifying manner. We introduce Ch…
Distantly Supervised Road Segmentation
Satoshi Tsutsui, Tommi Kerola, Shunta Saito
We present an approach for road segmentation that only requires image-level annotations at training time. We leverage distant supervision, which allows us to train our model using…